Quantifying processes of land-cover change by remote sensing: Resettlement and rapid land-cover changes in south-eastern Zambia

The objectives of this study are to quantify, based on remote sensing data, processes of land-cover change and to test a Markov-based model to generate short-term land-cover change projections in a region characterised by exceptionally high rates of change. The region of Lusitu, in the Southern Province of Zambia, has been a land-cover change 'hot spot' since the resettlement of 6000 people in the Lusitu area and the succession of several droughts. Land-cover changes were analysed on the basis of a temporal series of three multispectral SPOT images in three steps: (i) land-cover change detection was performed by combining the postclassification and image differencing techniques; (ii) the change detection results were examined in terms of proportion of land-cover classes, change trajectories and spatio-temporal patterns of change; (iii) the process of land-cover change was modelled by a Markov chain to predict land-cover distributions in the near future. The remote sensing approach allowed: (i) to quantify land-cover changes in terms of percentage of area affected and rates of change; (ii) to qualify the nature of changes in terms of impact on natural vegetation; (iii) to map the spatial pattern of land-cover change. 44% of the area has been affected by at least one change in land cover during the period 1986 to 1997. The average annual rate of land-cover change was 4.0%. Agricultural expansion was the dominant change process. Land-cover change trajectories highlighted the dynamic character of changes. The results obtained by applying a Markov chain for projecting future evolutions showed the continuing upward trend of bare soils and cultivated land, and the rapid downward trend of forests and other natural vegetation covers.     

Multivariate detection and attribution of land-cover changes in the Central Pilbara,Western Australia

The Multivariate Alteration Detection (MAD) method was applied to locate areas where land-cover changes occurred between 2003 and 2009 in the Central Pilbara, Western Australia. It was demonstrated that each of the six MAD variates contained information of land-cover changes at various spatial scales. This allowed attribution of the identified changes to particular stressors such as climate variability, fire events, and mining activity in the area. The results were analysed and interpreted using time series of multispectral normalized difference vegetation index, normalized difference wetness index, and normalized burn ratio grids derived from Landsat data observed over the study period. In addition, various ground truth data such as fire maps, historical climate data, and the available information about mine operations and water management, which could lead to alteration of natural water regime, were utilized.     

An assessment of impacts of land-cover changes on root-zone soil moisture

Land cover exerts considerable control over the exchange of energy, water, and carbon dioxide and other greenhouse gases between land surface and the atmosphere. In China, dramatic land-cover changes have occurred along with rapid economic development in the past 30 years. However, research specifically on whether such land-cover changes have any influence on root-zone soil moisture in the region has started only in very recent few years. In this study, the performance of selected land-surface models (Noah 2.7.1, Noah 3.2, Common Land Model (CLM version 2.0), and Mosaic) implemented in National Aeronautics and Space Administration (NASA)’s Land Information System (LIS version 6.1.6) is first tested using quality-controlled soil moisture observations from 108 in situ sites of the China Meteorological Administration. The best-performing model (CLM2.0) is selected to estimate the influence of land-cover changes on root-zone soil moisture, as well as drought occurrence in Yunnan Province in China. Both the 1992–1993 Advanced Very High Resolution Radiometer (AVHRR) and 2007–2010 Moderate Resolution Imaging Spectroradiometer Collection 5 (MODIS) land-cover products at 1 km resolution are employed to represent 1990 and 2010 land-cover status, respectively. These are verified using the local ground records of Yunnan Province over the two time periods. Their differences are considered roughly as land-cover changes occurring during the period 1990–2010. It is found that land-cover changes from primeval forest to grassland may increase root-zone soil moisture, thus reducing drought, while changes from grassland and primeval forest to cropland or reforested areas have increased the likelihood of drought.     

Frontal changes in the Manimahesh and Tal Glaciers in the Ravi basin,Himachal Pradesh,northwestern Himalaya (India), between 1971 and 2013

The Manimahesh and Tal Glaciers are located in the Budhil fifth-order sub-basin of the Ravi, Himachal Himalaya, Northwestern Himalaya (India). These glaciers were analysed using high- (Corona KH-4A) to medium- (Landsat TM/ETM+/OLI, ASTER) spatial resolution satellite data between 1971 and 2013, along with extensive field measurements (2011–2014) of frontal changes. The results show that the Manimahesh and Tal Glaciers retreated by 157 ± 34 m (4 ± 1 m year^–1) and 45 ± 34 m (1 ± 1 m year^–1), respectively, whereas, the total area lost is estimated at 0.21 ± 0.01 km² (0.005 km² year^–1) and 0.010 ± 0.003 km² (0.0002 km² year^–1), respectively, between 1971 and 2013. The rate of retreat is significantly lower than that previously reported. Our field measurements (2011–2014) also suggest a retreating trend and validate the measured glacier changes using remotely sensed temporal data.     

Assessing the spatio-temporal rates and patterns of land-use and land-cover changes in the Cerrados of southeastern Mato Grosso,Brazil

The Cerrados of central Brazil have undergone profound landscape transformation in recent decades due to agricultural expansion, and this remains poorly assessed. The present research investigates the spatial-temporal rates and patterns of land-use and land-cover (LULC) changes in one of the main areas of agricultural production in Mato Grosso State (Brazil), the region of Primavera do Leste. To quantify the different aspects of LULC changes (e.g. rates, types, and spatial patterns) in this region, we applied a post-classification change detection method, complemented with landscape metrics, for three dates (1985, 1995, and 2005). LULC maps were obtained from an object-based classification approach, using the nearest neighbour (NN) classifier and a multi-source data set for image object classification (e.g. seasonal Thematic Mapper (TM) bands, digital elevation model (DEM), and a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived index), strategically chosen to increase class separability. The results provided an improved mapping of the Cerrados natural vegetation conversion into crops and pasture once auxiliary data were incorporated into the classification data set. Moreover, image segmentation was crucial for LULC map quality, in particular because of crop size and shape. The changes detected point towards increasing loss and fragmentation of natural vegetation and high rates of crop expansion. Between 1985 and 2005, approximately 42% (6491 km²) of Cerrados in the study area were converted to agricultural land uses. In addition, it was verified that cultivated areas are encroaching into fragile environments such as wetlands, which indicates the intense pressure of agricultural expansion on the environment.     

Spatio-temporal reflectance fusion via unmixing: accounting for both phenological and land-cover changes

Owing to technical limitations the acquisition of fine spatial resolution images (e.g. Landsat data) with frequent (e.g. daily) coverage remains a challenge. One approach is to generate frequent Landsat surface reflectances through blending with coarse spatial resolution images (e.g. Moderate Resolution Imaging Spectroradiometer, MODIS). Existing implementations for data blending, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced STARFM (ESTARFM), have their shortcomings, particularly in predicting the surface reflectance characterized by land-cover-type changes. This article proposes a novel blending model, namely the Unmixing-based Spatio-Temporal Reflectance Fusion Model (U-STFM), to estimate the reflectance change trend without reference to the change type, i.e. phenological change (e.g. seasonal change in vegetation) or land-cover change (e.g. conversion of a vegetated area to a built-up area). It is based on homogeneous change regions (HCRs) that are delineated by segmenting the Landsat reflectance difference images. The proposed model was tested on both simulated and actual data sets featuring phenological and land-cover changes. It proved more capable of capturing both types of change compared to STARFM and ESTARFM. The improvement was particularly observed on those areas characterized by land-cover-type changes. This improved fusion algorithm will thereby open new avenues for the application of spatio-temporal reflectance fusion.     

Reflectances in the Ganges and Brahmaputra rivers and in the adjacent coastal sea

The distribution patterns of reflectance were analysed from Landsat TM band 3, along the course of the Ganges and Brahmaputra rivers and their adjacent coastal sea. It was observed that during the high discharge period, reflectance values in the Ganges river are higher than that in the Brahmaputra. But in the low discharge period, it is vice versa. Reflectances varied significantly throughout the river courses but increased distinctly at their confluence zones. In the coastal sea, reflectance values decreased markedly with a narrow zone, which corresponded to the zone of spectral contrast as seen from band 3 image. Influence of river discharge in reflectance values is not distinct in the coastal sea.     

Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data

The overarching goal of this study was to map irrigated areas in the Ganges and Indus river basins using near-continuous time-series (8-day), 500-m resolution, 7-band MODIS land data for 2001-2002. A multitemporal analysis was conducted, based on a mega file of 294 wavebands, made from 42 MODIS images each of 7 bands. Complementary field data were gathered from 196 locations. The study began with the development of two cloud removal algorithms (CRAs) for MODIS 7-band reflectivity data, named: (a) blue-band minimum reflectivity threshold and (b) visible-band minimum reflectivity threshold.A series of innovative methods and approaches were introduced to analyze time-series MODIS data and consisted of: (a) brightness-greenness-wetness (BGW) RED-NIR 2-dimensional feature space (2-d FS) plots for each of the 42 dates, (b) end-member (spectral angle) analysis using RED-NIR single date (RN-SD) plots, (c) combining several RN-SDs in a single plot to develop RED-NIR multidate (RN-MDs) plots in order to help track changes in magnitude and direction of spectral classes in 2-d FS, (d) introduction of a unique concept of space-time spiral curves (ST-SCs) to continuously track class dynamics over time and space and to determine class separability at various time periods within and across seasons, and (e) to establish unique class signatures based on NDVI (CS-NDVI) and/or multiband reflectivity (CS-MBR), for each class, and demonstrate their intra- and inter-seasonal and intra- and inter-year characteristics. The results from these techniques and methods enabled us to gather precise information on onset-peak-senescence-duration of each irrigated and rainfed classes.The resulting 29 land use/land cover (LULC) map consisted of 6 unique irrigated area classes in the total study area of 133,021,156 ha within the Ganges and Indus basins. Of this, the net irrigated area was estimated as 33.08 million hectares—26.6% by canals and 73.4z5 by groundwater. Of the 33.08 Mha, 98.4% of the area was irrigated during khariff (Southwest monsoonal rainy season during June-October), 92.5% irrigated during Rabi (Northeast monsoonal rainy season during November-February), and only 3.5% continuously through the year.Quantitative Fuzzy Classification Accuracy Assessment (QFCAA) showed that the accuracies of the 29 classes varied from 56% to 100%—with 17 classes above 80% accurate and 23 classes above 70% accurate.The MODIS band 5 centered at 1240 nm provided the best separability in mapping irrigated area classes, followed by bands 2 (centered at 859 nm), 7 (2130 nm) and 6 (1640 nm).     

Neotectonic analysis of Mendha river basin,Rajasthan, India

Abstract

The study presented here examined the tectonically active Mendha River basin, Rajasthan, India. Landsat TM data in bands 2, 3 and 4 were used in this study. The neotectonic features were extracted through the digital analysis of the principal component (PC) and directional filtered images. However, the regional view of the study area was examined through the false colour composite (FCC). A map of the neotectonic features was generated by incorporating the limited field observations and this yielded the extensions of the neotectonic features.     

Spatiotemporal analysis of land-use and land-cover change in the Brazilian Amazon

This paper provides a comparative analysis of land-use and land-cover (LULC) changes among three study areas with different biophysical environments in the Brazilian Amazon at multiple scales, from per-pixel, polygon, census sector, to study area. Landsat images acquired during the years of 1990/1991, 1999/2000, and 2008/2010 were used to examine LULC change trajectories with the post-classification comparison approach. A classification system composed of six classes – forest, savanna, other vegetation (secondary succession and plantations), agro-pasture, impervious surface, and water – was designed for this study. A hierarchical-based classification method was used to classify Landsat images into thematic maps. This research shows different spatiotemporal change patterns, composition, and rates among the three study areas and indicates the importance of analysing LULC change at multiple scales. The LULC change analysis over time for entire study areas provides an overall picture of change trends, but detailed change trajectories and their spatial distributions can be better examined at a per-pixel scale. The LULC change at the polygon scale provides the information of the changes in patch sizes over time, while the LULC change at census sector scale gives new insights on how human-induced activities (e.g. urban expansion, roads, and land-use history) affect LULC change patterns and rates. This research indicates the necessity to implement change detection at multiple scales for better understanding the mechanisms of LULC change patterns and rates.     

Evaluation of dynamic linkages between evapotranspiration and land-use/land-cover changes with Landsat TM and ETM+ data

Complexity embedded in coastal management leads to numerous questions as to how inherent spatial and temporal linkages among evapotranspiration (ET), depth to groundwater and land-use/land-cover change (LUCC) could affect the dynamics among these seemingly unrelated events. This article aims to address such unique dynamics in the nexus of physical geography and ecohydrology. To understand such dynamic linkages, a case study was carried out in a fast growing coastal region – the southern Laizhou Bay in Shandong Province, China – by identifying the coastal LUCC at the decadal scale in association with the variations of ET with the aid of Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) data. In such a coastal landscape evolutionary assessment, findings show that the major patterns of land use and land cover (LULC) in the study area are farmland, saline-alkali land, developed land, salt land and beach land. Over a 20-year time frame, declining groundwater trends were observed, while ET increased gradually with changing LULC. By using the surface energy balance algorithm for land (SEBAL) with Landsat TM/ETM+ images and additional environmental data, the concomitant response of ET variations due to LUCC becomes lucid among three significantly correlated pairs including fractional vegetation cover (FVC), land surface temperature (LST) and soil heat flux. The dynamic linkages between ET and LULC were finally confirmed with such a pair-wise analysis.     

Monitoring land-cover changes in West Africa with SPOT Vegetation: Impact of natural disasters in 1998-1999

A multi-temporal change-vector algorithm has been applied to SPOT Vegetation data of West Africa over two growing seasons. Change maps displaying the impact on surface attributes of natural disasters such as floods and droughts were produced, based on different vegetation indices and compositing periods. The accuracy assessed on the basis of independent data on natural disasters, was high (76% correct for the NDVI-based map). This land-cover change product provides important information to evaluate the spatial and temporal distribution of rapid changes in land surface attributes.     

India,TRIPS-plus free trade agreements and the future of access to essential medicines

The issue of TRIPS-plus standards and its implications of access to essential medicines are discussed in this article in the Indian context. As a leading country in the manufacture and supply of generic drugs, the case of India is crucial when considered from the perspective of the TRIPS-plus regime and its negative consequences. Given the absence of a free trade agreement between the USA and India, a hypothetical approach is adopted in this article to analyse the potential implications of such an agreement in the future. The argument extended in this article relates to the existing norms of TRIPS-plus standards in US trade negotiations and an analysis is provided about the application of a TRIPS-plus model in the Indian context. It is argued that both for public health and trade policy reasons, India should not engage in any TRIPS-plus trade agreement with the USA as it would harm its interests on domestic and foreign fronts. India has recently adopted a new patent policy and a reasonable time should be given to relevant institutions to build an operational framework and capacity before further changes are made. This task will not be less than challenging as there is very little evidence about any positive change in the position of the USA and it would continue imposing TRIPS-plus standards through a variety of trade instruments. It is further argued in this article that the best mitigating strategy for developing countries like India lies in the combination of multilateralism and networking along the lines of a rights-based approach.     

Land transitions from multivariate time series: using seasonal trend analysis and segmentation to detect land-cover changes

The detection of land-cover change over large areas using short time series is a challenging and important task in global change studies. This paper introduces a novel method, called the Segment-based Detection of Trends and Change (SDTC), to determine areas that are undergoing land-cover changes. The method is illustrated in the State of Alaska over the 2001–2009 time period using three time series derived from Moderate Resolution Spectroradiometer (MODIS) imagery: the normalized difference vegetation index (NDVI), albedo, and land surface temperature (LST). SDTC extends seasonal trend analysis (STA) by using segmentation in the detection process to combine multiple variables and to incorporate the spatial context. Segments labelled as ‘change’ correspond to groups of adjacent pixels with a majority of pixels undergoing significant temporal trends as defined by the Mann–Kendall (MK) procedure and STA. Segments correspond to landscape units in a less arbitrary manner than pixels because they represent groupings of adjacent areas undergoing similar temporal behaviour. Findings indicate that the use of MK in conjunction with segmentation exploits more fully the richness of the spatial context in the process of detection. Results suggest that SDTC is a useful method for detecting and characterizing land-cover change. The technique is conservative and eliminates the ‘salt and pepper’ effect by filtering noise and identifying areas of change. Using SDTC, we found that most areas of change in Alaska undergo between one and three significant changes and that increasing LST seasonality constitutes the most widespread type of change.     

Exploring the potential role of feature selection in global land-cover mapping

Global land cover has been acknowledged as a fundamental variable in several global-scale studies for environment and climate change. Recent developments in global land-cover mapping focused on spatial resolution improvement with more heterogeneous features to integrate the spatial, spectral, and temporal information. Although the high dimensional input features as a whole lead to discriminatory strengths to produce more accurate land-cover maps, it comes at the cost of an increased classification complexity. The feature selection method has become a necessity for dimensionality reduction in classification with large amounts of input features. In this study, the potential of feature selection in global land-cover mapping is explored. A total of 63 features derived from the Landsat Thematic Mapper (TM) spectral bands, Moderate Resolution Imaging Spectroradiometer (MODIS) time series enhanced vegetation index (EVI) data, digital elevation model (DEM), and many climate-ecological variables and global training samples are input to k-nearest neighbours (k-NN) and Random Forest (RF) classifiers. Two filter feature selection algorithms, i.e. Relieff and max-min-associated (MNA), were employed to select the optimal subsets of features for the whole world and different biomes. The mapping accuracies with/without feature selection were evaluated by a global validation sample set. Overall, the result indicates no significant accuracy improvement in global land-cover mapping after dimensionality reduction. Nevertheless, feature selection has the capability of identifying useful features in different biomes and improves the computational efficiency, which is valuable in global-scale computing.     

Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems

The objective of this paper is to review research conducted over the past decade on the application of multi-temporal remote sensing for monitoring changes of Arctic tundra lands. Emphasis is placed on results from the National Science Foundation Land-Air-Ice Interactions (LAII) program and on optical remote sensing techniques. Case studies demonstrate that ground-level sensors on stationary or moving track platforms and wide-swath imaging sensors on polar orbiting satellites are particularly useful for capturing optical remote sensing data at sufficient frequency to study tundra vegetation dynamics and changes for the cloud prone Arctic. Less frequent imaging with high spatial resolution instruments on aircraft and lower orbiting satellites enable more detailed analyses of land cover change and calibration/validation of coarser resolution observations.The strongest signals of ecosystem change detected thus far appear to correspond to expansion of tundra shrubs and changes in the amount and extent of thaw lakes and ponds. Changes in shrub cover and extent have been documented by modern repeat imaging that matches archived historical aerial photography. NOAA Advanced Very High Resolution Radiometer (AVHRR) time series provide a 20-year record for determining changes in greenness that relates to photosynthetic activity, net primary production, and growing season length. The strong contrast between land materials and surface waters enables changes in lake and pond extent to be readily measured and monitored.     

Exploring the addition of Landsat 8 thermal band in land-cover mapping

One focus of remote-sensing studies is obtaining highly accurate land-cover maps, which is essential for quantifying and monitoring changes in the environment. However, thermal data, which can provide auxiliary information, is often ignored in land-cover classification. In this study we compare the performance of different remote-sensing feature combinations with and without the Landsat 8 thermal band (band 10). The results show that overall the thermal feature had a positive effect on mapping land cover. A combination of spectral features, indices and the thermal feature maximized the improvement in accuracy. The proposed classifier was applied to map land cover in an area in Egypt. The thermal feature significantly reduced the confusion between cropland and wetland. The improvement in accuracy obtained by adding the thermal feature was analysed in a time series spanning 1 year. We found that the thermal feature improved the classification accuracy when temperature variations occurred among the different land-cover types. The effect of the thermal feature was also influenced by the land cover; in cloudless conditions, warmer weather can enhance the accuracy improvement of the thermal feature.     

Thermal inertia mapping over the Brahmaputra basin,India using NOAA-AVHRR data and its possible geological applications

Thermal inertia is a volume property and shows the resistance power of the material against changes in its temperature. The thermal inertia of a surficial feature of interest cannot be directly measured. Hence, a proper modelling is required for its estimation. The objective of the project is to develop a technique to generate thermal inertia images using available National Oceanic and Atmospheric Administration (NOAA) satellite data to detect thermal anomalies and oilfield signature over a known producing basin. The Brahmaputra valley in Upper Assam is selected for this study.

NOAA-Advanced Very High Resolution Radiometer (AVHRR) thermal data were converted to temperature, based on the look-up table (LUT) given in the NOAA-AVHRR CD and by using split-window atmospheric attenuation correction models. The thermal inertia imagery is constructed with the help of the albedo imagery generated from the daytime and with the knowledge of the surface temperature change between the daytime and night-time data. The thermal inertia values are computed for all pixels common to both daytime and night-time and the thermal inertia imagery generated for the study area. The thermal inertia of a surface cannot be measured directly; so another model is also used to estimate apparent thermal inertia (ATI). The images from both the models have shown similar results.

The geological map when draped over the ATI image shows good correlation of gross lithology and thermal inertia. The metamorphics/basement and the sediments are well differentiated by their tonal and textural characters. The Mikir massif shows conspicuously brighter signature than the featureless darker signatures of the surrounding valley. Within the valley, the river water exhibits bright tone, whereas the present-day sandbars within the river exhibit darker tone than the alluvial plains of the valley. This is in agreement with the available published data. Major thrusts can be mapped as bright linear tone, and their geometry coincides well with those mapped in the field. Exposed cross faults can also be mapped in Arunachal foothills and faults in Mikir massif. The isoneotectonic map when draped over the ATI image shows that the identified isoneotectonic units can be well differentiated in the image on the basis of tonal characters. The prominent lineaments mapped in Mikir massif can be traced in the valley part also.

The producing and dry structures in the valley show very few signatures on the thermal inertia images, possibly due to poor spectral and spatial resolution of the NOAA data. It is planned to use the developed technique to generate thermal inertia maps using higher spatial and spectral resolution satellite data (e.g. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)), which may provide better oilfield signatures.     

Flood monitoring using microwave passive remote sensing (AMSR-E) in part of the Brahmaputra basin,India

In this work, Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) signatures were analysed over some critical sites in Lakhimpur District in Brahmaputra basin, India, characterized by a high frequency of flooding events. The site is mostly covered by paddy fields. Results obtained were compared with water level measurements in three stations close to the main channel of the river. Information about surface temperature, which allowed us to estimate the emissivity, was also available. Investigations were carried out at the C, X, and K_a bands of the AMSR-E channel. A multi-frequency analysis indicated that the X band would represent a good compromise between resolution and sensitivity requirements, while at the C band the resolution was too coarse and at the K_a band the signatures were affected by raindrops. Samples collected during rain were eliminated using techniques based on the 89.0 GHz channel. However, even after this correction, the K_a band showed poor sensitivity due to higher attenuation by vegetation. The correlations between different pairs of variables, viz. polarization index (PI), water level (WL), and fractional water surface area (F _WS), were also investigated. At the X band, the water level was better correlated with the PI than with emissivity and other parameters defined in the literature. The correlation was good in cases of slow variation in WL. In cases of sudden variation in the river, the PI followed the variations with some time delay related to the propagation of water within the covered AMSR-E pixel.